Go from zero to a structured AI system blueprint in one shot. Agent definitions, workflow maps, trust models — all pre-structured for your domain and persona.
01 / UNDERSTAND
System intelligence
Domain context, workflows, and stakeholder decisions — pre-mapped before you open Figma.
02 / DEFINE
Agent behavior
Goal, inputs, reasoning type, actions, and human-in-loop boundaries — structured, not guessed.
03 / EXPLORE
Visual workflow canvas
Edit as-is and to-be flows. Simulate "what if" scenarios. See AI opportunities mapped per step.
Configure your project
Tell Oneshot about your domain and problem. The more specific, the richer your kit.
Industry
Primary Persona Role
Suggested AI Use Cases from 214 enterprise deployments — click to select
Problem Statement
AI Capabilities Needed
Diagnose
Predict
Recommend
Explain
Automate
Plan
Monitor
Summarize
Risk Level
Low — reversible, low stakes
Medium — operational impact
High — safety / financial / regulatory
Building your intelligence pack
Mapping domain context, workflows, and agent behavior
Foundation
Problem Brief
Decision Persona
System
Workflow Canvas
AI Opportunity Map
Intelligence
Agent Definition
Multi-Agent Map
Future Workflow
Design
Interaction Model
Design Options
Trust & Risk Layer
Outcome Metrics
Explore
What If Mode
Problem Brief
The clarity anchor for the entire project
Design reasoning
Why this framing
RCA is a decision problem, not a data problem. The AI must explain root cause, not just surface anomalies — this shifts the design from dashboard to diagnostic partner.
Problem type
Decision gap — the expert has data but lacks synthesis and cross-signal correlation at speed.
Refined problem statement
Data administrators managing Snowflake/Databricks environments spend 2–3 hours per incident diagnosing performance regressions. Investigation requires manually correlating signals across 6+ tools with no unified context. Root cause is often discovered too late, after downstream SLA breaches.
Business context
Each delayed investigation costs estimated $12K–$40K in SLA penalties and engineering hours. At scale, 3–5 incidents/week compounds into significant operational risk.
Problem type
Decision gap — not a data visibility issue. Data exists. The gap is synthesis, correlation, and explanation at expert speed.
Decision-Centric Persona
Not a generic persona — defined by decisions, not demographics
Design reasoning
Why decision-centric
AI systems support decisions, not just tasks. Defining what Ria must decide under time pressure shapes what the agent must surface, and when.
Persona
Ria, Senior Data Administrator
7 years managing multi-cloud warehouse environments. Expert in query optimization but overwhelmed by cross-cluster anomaly correlation at incident scale.
Is this a query, warehouse, or infrastructure problem?
Root classification
High risk
Which downstream pipelines are at risk right now?
Impact scope
Medium risk
What remediation should I apply and in what order?
Action selection
High risk
What she struggles to interpret
Cross-cluster signal correlation without a unified timeline. Distinguishing query-level vs. warehouse-level vs. network-level root cause. Knowing when a pattern is signal vs. noise at 3am.
Workflow Canvas
Visual as-is / to-be flows — click any step to explore it
As-Is Workflow
Future (AI-Enhanced)
AS-IS · Total avg: 2–3 hours · Tools: 6+ · Confidence at step 05: ~40%
Step detail
⚠ High pain zone at steps 02–04 — manual investigation accounts for ~80% of total incident time
AI-ENHANCED · Total avg: ~27 min · Automated: 3 of 5 steps · Human retains final decision
Step detail
Agent-drivenHuman decisionUnchanged
AI Opportunity Map
Where AI fits — per workflow step, with role and value
Design reasoning
Why this prioritization
Steps 03 and 04 are the highest-effort, lowest-confidence steps. AI automation here has the highest ROI and the lowest trust risk — it surfaces evidence, not conclusions.
Agent Definition
The core differentiator — how the AI behaves, not just what it shows
RCA INTELLIGENCE AGENT v1
Live confidence: 78%
Agent goal
Reduce mean time to root cause (MTTRC) from 2–3 hours to under 30 minutes by autonomously correlating signals and presenting ranked, explained hypotheses for human review.
Auto Signal ingestion, deduplication, timeline assembly
Suggest Ranked root cause hypotheses with evidence
Human Remediation execution always human-confirmed
Human-in-loop boundaries
Never automated: Final root cause declaration · Remediation execution · Incident escalation
Always shown: Confidence score per hypothesis · Evidence trail · Alternative hypotheses · Data freshness timestamps
Multi-Agent Map
Swarm architecture — parallel agents, specializations, handoffs, and convergence point
Design reasoning
Why swarm, not single agent
RCA requires simultaneous investigation across independent signal domains. A single agent reasoning sequentially across 6 data sources is slower and more brittle than 4 specialized agents running in parallel and converging. Swarm reduces MTTRC and isolates failure — if one agent fails, the others still produce partial output.
Human role in the swarm
The human is the convergence point — not a passive receiver. Ria reviews aggregated agent output, applies domain judgment, and either accepts the swarm's consensus or overrides with her own root cause. Her decision is the only irreversible action in the system.
SWARM TOPOLOGY · RCA INTELLIGENCE SYSTEM
Agent registry
Agent 01 — Signal Ingestion
Type: Automated · No human loop Inputs: Snowflake, Grafana, PagerDuty, Databricks Output: Unified event timeline Failure mode: Partial timeline — surface missing sources to Ria
Type: Synthesis · LLM reasoning over agent outputs Inputs: All agent outputs + runbook knowledge base Output: Ranked hypothesis list with aggregated confidence Failure mode: If conflict between agents — surface both, flag disagreement
Agent 06 — Explainer
Type: Explanation · LLM narration Inputs: Ranked hypotheses + evidence chain Output: Human-readable explanation card per hypothesis Failure mode: Surface raw data without explanation, flag to Ria
Swarm-specific design risks
Agent conflict — agents 02 and 03 produce contradictory hypotheses
Design for
Partial failure — one agent returns no output or low confidence
Explainer hallucination — narration diverges from raw evidence
Design for
Future Workflow
The to-be system — switch to the Future layer in the Workflow Canvas to see this visually
Design reasoning
Transformation principle
The agent handles synthesis. Ria handles judgment. Every automated step still produces a human-readable artifact she can audit. Speed comes from compression, not from removing oversight.
What changed
Signal ingestion (45–90 min)
Automated
→ 30 sec
Cross-signal correlation (30–60 min)
Automated
→ 90 sec
Root cause decision
Assisted
→ 5 min
Remediation selection
Assisted
→ 5 min
Remediation execution
Human
Unchanged
2–3h
Before
→
27min
After
Interaction Model
UI/UX direction — screen design decisions become obvious from here
Design reasoning
Why hybrid, not conversational
Ria is an expert, not a novice. She doesn't want to prompt an AI — she wants a diagnostic cockpit that surfaces the hypothesis with evidence she can accept or override. Conversation slows her down.
Why structured over open-ended
High-stakes decisions require legible AI outputs. A ranked hypothesis card with confidence bars and evidence links is auditable. A chat response is not.
Interaction type
Hybrid: Dashboard + Diagnostic Panel
Structured overview with expandable evidence drill-down. No open prompt interface.
Key UI moments
1. Incident card with top hypothesis
2. Evidence timeline (auto-assembled)
3. Confidence bar + alternative hypotheses
4. Accept / Override / Escalate actions
5. Remediation playbook with 1-click staging
Feedback loops
Ria's override decisions feed back into the model. Every accepted/rejected hypothesis improves future ranking. Feedback is passive — no extra effort from the user.
Design Options
3 directions for this problem context — select, attach a design system, then open in Figma
Design reasoning
Why these 3
Each option represents a different trust posture and interaction density. A is system-based and familiar. B is generated for expert speed under pressure. C is minimal — low cognitive load for high-stress environments.
Select options to export → attach a design system → open in Figma
Operational impact risk. Incorrect root cause delays remediation. Mitigated by: human always executes.
Always
Confidence shown
Every hypothesis shows a % confidence score, evidence count, and data freshness. No black-box outputs.
Visible
Override mechanism
One-tap override on any AI suggestion. Override reason captured. All overrides audit-logged.
Explanation strategy
Show evidence, not just conclusions. For each hypothesis: which signals triggered it, what time window, what similar past incidents confirm it. Designed for expert validation, not novice consumption.
Failure handling
If confidence < 40%: surface "Low confidence — manual review recommended" state. Never present a low-confidence result as a definitive answer. Graceful degradation to partial evidence assembled.
Outcome Metrics
Connects design decisions → business value
−82%
Investigation time
71%
Recommendation acceptance rate
3.2×
Incidents resolved per shift
34→71%
Agent trust score (90 days)
Behavioral change target
Ria stops context-switching between 6 tools. She opens one interface, reviews AI output, makes a judgment call, and moves to execution. The job shifts from information gathering to decision quality.
What If Mode
Scenario exploration — change the system, simulate outcomes
Select a scenario to explore its outcome
Higher automation level
Agent auto-executes remediation without human confirmation
Lower confidence threshold
Agent surfaces hypotheses at <40% confidence
Conversational interface
Replace dashboard with open chat prompt interface
No explanation layer
Show ranked hypotheses without evidence or reasoning
Simulated outcome
If the agent auto-executes remediation without human confirmation: adoption collapses within weeks. Trust is the product. In high-stakes ops environments, a single wrong automated action creates lasting resistance. The speed gain (~5 min) is not worth the trust cost. Design decision: keep human execution, accelerate the decision step instead.
Oneshot Copilot
Oneshot Copilot
Ask me anything about this starter kit — agent goals, trust model, design options, outcome metrics, or the workflow map.